code: def main():
      X, y = make_classification(
          10000, n_features=100, n_informative=50, random_state=0
      )
      X = X.astype(np.float32)
  
      module = MyModule()
      accelerator = Accelerator()
      net = Net(module, accelerator)
      # cross_validate creates a deepcopy of the accelerator attribute
      res = cross_validate(
          net,
          X,
          y,
          cv=2,
          scoring="accuracy",
          verbose=3,
          error_score="raise",
      )
      print(res)
  
file_path: skorch/examples/accelerate-multigpu/run-no-skorch.py
imports: import numpy
  import torch
  from accelerate import Accelerator
  from sklearn.datasets import make_classification
  from sklearn.model_selection import cross_validate
  from sklearn.base import BaseEstimator
  from torch import nn
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types